1 rANOMALY step-by-step use case.

1.1 Help

Each function have a detailed help accessible in R via ?{funtion}.

1.2 Tests datasets

The dataset can be downloaded via this link.

This tutorial assume that you have extracted all the read file in a folder named reads along with the sample-metadata.csv file.

We share a 24 samples test dataset extract from rats feces at two different time (t0 & t50) and in two nutrition conditions. Also included two extraction control sample (blank).

sm <- read.table("sample_metadata.csv", sep="\t",header=TRUE)
DT::datatable(sm)
load("decontam_out/robjects.Rdata")

1.3 ASV definition with DADA2

The first step will be the creation of ASVs (Amplicon Sequence Variants) thanks to the dada2 package. In rANOMALY, only one function is needed to compute all the different steps require from this package.

Sample names will be extracted from the file name, so files must be formatted as followed : {sample-id1}_R1.fastq.gz {sample-id1}_R2.fastq.gz etc…

dada_res = dada2_fun(path="./reads", dadapool = "pseudo", compress=TRUE, plot=FALSE)

Main output: - read_tracking.csv that summarize the read number after each filtering step.

DT::datatable(read.table("dada2_out/read_tracking.csv",sep="\t",header=TRUE))

The sample names extracted from the file name. We consider as sample name anything that is before the first underscore. This must match the sample names that are in sample metadata files. input: raw read number. filtered: after dada2 filtering step: no N’s in sequence, low quality, and phiX. denoisedF & denoisedR: after denoising. Forward & Reverse. merged: after merging R1 & R2. nonchim: after chimeras filtering.

  • dada2_robjects.Rdata with raw ASV table and representative sequences in objects otu.table, seqtab.export & seqtab.nochim.
  • raw_asv-table.csv
  • rep-seqs.fna

1.4 Taxonomic assignment

This function uses IDTAXA function from DECIPHER package, and allows to use 2 differents databases. It keeps the best assignation on 2 criteria, resolution (depth) and confidence. The final taxonomy is validated by multiple ancestors taxa and incongruity correction step.

We share the latest databases we use in the IDTAXA format in this link. You can also generate your own database following those instructions and scripts we provide in another repository.

tax.table = assign_taxo_fun(dada_res = dada_res, id_db = c("path_to_your_banks/silva/SILVA_SSU_r132_March2018.RData","path_to_your_banks/DAIRYdb_v1.2.0_20190222_IDTAXA.RData") )

Main output: - taxo_robjects.Rdata with taxonomy in phyloseq format in tax.table object. - final_tax_table.csv the final assignation table that will be use in next steps. - allDB_tax_table.csv raw assignations from the two databases, mainly for debugging.

1.5 Phylogenetic Tree

The phylogenetic tree from the representative sequences is generated using phangorn and DECIPHER packages.

tree = generate_tree_fun(dada_res)

Main output: - tree_robjects.Rdata with phylogenetic tree object in phyloseq format.

1.6 Phyloseq object

To create a phyloseq object, we need to merge four objects and one file: - the asv table otu.table and the representative sequences seqtab.nochim from dada2_robjects.Rdata - a taxonomy table taxo_robjects.Rdata from taxo_robjects.Rdata - the phylogenetic tree tree from tree_robjects.Rdata - metadata from sample-metadata.csv

data = generate_phyloseq_fun(dada_res = dada_res, taxtable = tax.table, tree = tree, metadata = "./sample_metadata.csv")

Main output: - robjects.Rdata with phyloseq object in data for raw counts and data_rel for relative abundance.

1.7 Decontamination

The decontam_fun function uses decontam R package with control samples to filter contaminants. The decontam package offers two main methods, frequency and prevalence (and then you can combine those methods). For frequency method, it is mandatory to have the dna concentration of each sample in phyloseq (and hence in the sample-metadata.csv). “In this method, the distribution of the frequency of each sequence feature as a function of the input DNA concentration is used to identify contaminants.” In the prevalence methods no need of DNA quantification. “In this method, the prevalence (presence/absence across samples) of each sequence feature in true positive samples is compared to the prevalence in negative controls to identify contaminants.

Tips: sequencing plateforms often quantify the DNA before sequencing, but do not automaticaly give the information. Just ask for it ;).

Our function integrates the basics ASV frequency (nb_reads_ASV/nb_total_reads) and prevalence (nb_sample_ASV/nb_total_sample) filtering. As in our lab we had a known recurrent contaminant we included an option to filter out ASV based on they taxa names.

data = decontam_fun(data = data, domain = "Bacteria", column = "type", ctrl_identifier = "control", spl_identifier = "sample", number = 100)

Main output: - robjects.Rdata with contaminant filtered phyloseq object named data. - Exclu_out.csv list of filtered ASVs for each filtering step. - Kronas before and after filtering. - raw_asv-table.csv & relative_asv-table.csv. - venndiag_filtering.png.

venndiag

venndiag

1.8 Plots, diversity and statistics

!!! We are currently developping a ShinyApp to visualize your data, sub-select your samples/taxons and do all those analyses interactively !!! ExploreMetabar

1.8.1 Rarefaction curves

In order to observe the sampling depth of each samples we start by plotting rarefactions curves. Those plots are generated by Plotly which makes the plots interactive.

rarefaction(data, "souche_temps", 100 )
## rarefying sample SB1-Sauv0
## rarefying sample SB10-Mut0
## rarefying sample SB11-Mut0
## rarefying sample SB12-Mut0
## rarefying sample SB13-Sauv50
## rarefying sample SB14-Sauv50
## rarefying sample SB15-Sauv50
## rarefying sample SB16-Sauv50
## rarefying sample SB17-Sauv50
## rarefying sample SB18-Sauv50
## rarefying sample SB19-Mut50
## rarefying sample SB2-Sauv0
## rarefying sample SB20-Mut50
## rarefying sample SB21-Mut50
## rarefying sample SB22-Mut50
## rarefying sample SB23-Mut50
## rarefying sample SB24-Mut50
## rarefying sample SB3-Sauv0
## rarefying sample SB4-Sauv0
## rarefying sample SB5-Sauv0
## rarefying sample SB6-Sauv0
## rarefying sample SB7-Mut0
## rarefying sample SB8-Mut0
## rarefying sample SB9-Mut0
## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

1.8.2 Composition plots

Composition plots reveals here the top 10 genus present in our samples. #TODO Ord1 option control the… Fact1 option control the…

1.8.2.1 Relative abundance

bars_fun(data = data, top = 10, Ord1 = "souche_temps", Fact1 = "souche_temps", rank="Genus", relative = TRUE)

1.8.2.2 Raw abundance

bars_fun(data = data, top = 10, Ord1 = "souche_temps", Fact1 = "souche_temps", rank="Genus", relative = FALSE)

1.8.3 Diversity analyses

1.8.3.1 Alpha diversity

This function computes various alpha diversity indexes and returns: - a boxplot comparing conditions. - a table of values - an ANOVA analysis - a wilcox result test comparing conditions and giving the significativity of the observed differences. - a mixture model if your data include repetition in sampling. All this in a single function.

alpha <- diversity_alpha_fun(data = data, output = "./plot_div_alpha/", column1 = "souche", column2 = "temps",
                    column3 = "", supcovs = "", measures = c("Observed", "Shannon") )
## [1] "coucou"
## INFO [2020-08-20 16:22:24] Alpha diversity tab ...
## INFO [2020-08-20 16:22:24] Done.
## INFO [2020-08-20 16:22:24] Plotting ...
## INFO [2020-08-20 16:22:24] Done.
## INFO [2020-08-20 16:22:25] ANOVA ...
## INFO [2020-08-20 16:22:25] Done.
## INFO [2020-08-20 16:22:25] Finish.
1.8.3.1.1 Table of values

The table of values for each indices you choose to compute.

pander(alpha$alphatable, style='rmarkdown')
  Observed Shannon
SB1.Sauv0 41 1.477
SB10.Mut0 40 2.073
SB11.Mut0 51 2.178
SB12.Mut0 38 2.116
SB13.Sauv50 46 2.691
SB14.Sauv50 57 2.905
SB15.Sauv50 50 2.793
SB16.Sauv50 52 2.8
SB17.Sauv50 49 2.624
SB18.Sauv50 54 2.831
SB19.Mut50 66 2.638
SB2.Sauv0 26 2.099
SB20.Mut50 72 2.721
SB21.Mut50 79 3.062
SB22.Mut50 81 2.81
SB23.Mut50 84 3.175
SB24.Mut50 90 3.148
SB3.Sauv0 19 0.1962
SB4.Sauv0 41 2.52
SB5.Sauv0 46 1.923
SB6.Sauv0 46 1.067
SB7.Mut0 33 2.256
SB8.Mut0 58 2.089
SB9.Mut0 50 2.237

1.8.3.2 Boxplots

The boxplots of those values.

alpha$plot

1.8.3.3 ANOVA results

For each indices, you have access to the ANOVA test. Here we present the result for the “Observed” indice.

pander(alpha$Observed$anova)
Analysis of Variance Model
  Df Sum Sq Mean Sq F value Pr(>F)
Depth 1 49.36 49.36 0.5091 0.4838
souche 1 1877 1877 19.36 0.0002764
temps 1 3649 3649 37.64 5.392e-06
Residuals 20 1939 96.96 NA NA

1.8.3.4 Wilcox test

Wilcox tests are made on each factor you have entered, and the combination of the two. Here “souche” and “temps”.

1.8.3.4.1 Wilcox test for “souche” factor
pander(alpha$Observed$wilcox_col1)
  mutant
sauvage 0.043
1.8.3.4.2 Wilcox test for “temps” factor FDR corrected.
pander(alpha$Observed$wilcox_col2_fdr)
  t0
t50 0.001
1.8.3.4.3 Wilcox test for the collapsed factors
pander(alpha$Observed$wilcox_col2_collapsed)
  mutant_t0 mutant_t50 sauvage_t0
mutant_t50 0.002 NA NA
sauvage_t0 0.377 0.005 NA
sauvage_t50 0.336 0.002 0.008

1.8.3.5 Beta diversity

beta <- diversity_beta_fun(data = data, output = "./plot_div_beta/", glom = "ASV", column1 = "temps", column2 = "souche", covar ="")
## INFO [2020-08-20 16:22:26] Option1...
## [1] "t0"  "t50"
## INFO [2020-08-20 16:22:26] Split table t0...
## INFO [2020-08-20 16:22:26] Done.
## [1] ""
## INFO [2020-08-20 16:22:26] No glom ...
## INFO [2020-08-20 16:22:26] Bray ...
## 
##  mutant sauvage 
##       6       6 
## INFO [2020-08-20 16:22:27] Done
## INFO [2020-08-20 16:22:27] Unifrac ...
## INFO [2020-08-20 16:22:27] Done
## INFO [2020-08-20 16:22:27] wunifrac ...
## INFO [2020-08-20 16:22:27] Done
## 
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2  Pr(>F)  
## Depth      1   0.53973 0.53973  2.8355 0.17954 0.02398 *
## souche     1   0.75338 0.75338  3.9580 0.25061 0.01199 *
## Residuals  9   1.71311 0.19035         0.56985          
## Total     11   3.00623                 1.00000          
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
##               pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1  0.952842 4.640344 0.3169559   0.008      0.008   *
## 
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs  MeanSqs F.Model      R2  Pr(>F)  
## Depth      1   0.12045 0.120447  1.6362 0.12272 0.14486  
## souche     1   0.19850 0.198504  2.6965 0.20225 0.01299 *
## Residuals  9   0.66253 0.073615         0.67503          
## Total     11   0.98148                  1.00000          
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
##               pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1 0.2429196 3.289082 0.2475026   0.003      0.003   *
## 
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)   
## Depth      1   0.51694 0.51694  5.3962 0.32059 0.002997 **
## souche     1   0.23337 0.23337  2.4360 0.14472 0.059940 . 
## Residuals  9   0.86218 0.09580         0.53469            
## Total     11   1.61249                 1.00000            
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
##               pairs Df SumsOfSqs  F.Model       R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1 0.3815338 3.099498 0.236612   0.045      0.045   .
## INFO [2020-08-20 16:22:27] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1383252 
## Run 1 stress 0.1415928 
## Run 2 stress 0.138325 
## ... New best solution
## ... Procrustes: rmse 0.0008296711  max resid 0.001748451 
## ... Similar to previous best
## Run 3 stress 0.2163921 
## Run 4 stress 0.1383259 
## ... Procrustes: rmse 0.0002572591  max resid 0.0005194362 
## ... Similar to previous best
## Run 5 stress 0.1471255 
## Run 6 stress 0.1383278 
## ... Procrustes: rmse 0.00116331  max resid 0.002057887 
## ... Similar to previous best
## Run 7 stress 0.1383248 
## ... New best solution
## ... Procrustes: rmse 0.0002843611  max resid 0.0006506299 
## ... Similar to previous best
## Run 8 stress 0.1383249 
## ... Procrustes: rmse 0.0001756664  max resid 0.0004123756 
## ... Similar to previous best
## Run 9 stress 0.1416605 
## Run 10 stress 0.2136644 
## Run 11 stress 0.1383256 
## ... Procrustes: rmse 0.0005725732  max resid 0.00107865 
## ... Similar to previous best
## Run 12 stress 0.1416638 
## Run 13 stress 0.1415945 
## Run 14 stress 0.2172086 
## Run 15 stress 0.1383266 
## ... Procrustes: rmse 0.001067344  max resid 0.001929848 
## ... Similar to previous best
## Run 16 stress 0.2451062 
## Run 17 stress 0.1383281 
## ... Procrustes: rmse 0.001509928  max resid 0.002812518 
## ... Similar to previous best
## Run 18 stress 0.2288809 
## Run 19 stress 0.1416399 
## Run 20 stress 0.1383251 
## ... Procrustes: rmse 0.000555828  max resid 0.001111333 
## ... Similar to previous best
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1383248 
## Run 1 stress 0.2307729 
## Run 2 stress 0.1383264 
## ... Procrustes: rmse 0.001278423  max resid 0.002377285 
## ... Similar to previous best
## Run 3 stress 0.1383276 
## ... Procrustes: rmse 0.001697241  max resid 0.003017132 
## ... Similar to previous best
## Run 4 stress 0.1383247 
## ... New best solution
## ... Procrustes: rmse 0.0001894508  max resid 0.0004286926 
## ... Similar to previous best
## Run 5 stress 0.141657 
## Run 6 stress 0.2172085 
## Run 7 stress 0.1383248 
## ... Procrustes: rmse 0.0001797545  max resid 0.0003307533 
## ... Similar to previous best
## Run 8 stress 0.2147061 
## Run 9 stress 0.1415924 
## Run 10 stress 0.1416469 
## Run 11 stress 0.1383253 
## ... Procrustes: rmse 0.0005195144  max resid 0.001271579 
## ... Similar to previous best
## Run 12 stress 0.2116428 
## Run 13 stress 0.1416665 
## Run 14 stress 0.1383248 
## ... Procrustes: rmse 9.677855e-05  max resid 0.0001383072 
## ... Similar to previous best
## Run 15 stress 0.1415946 
## Run 16 stress 0.1383253 
## ... Procrustes: rmse 0.0004864877  max resid 0.00118181 
## ... Similar to previous best
## Run 17 stress 0.138325 
## ... Procrustes: rmse 0.0004131526  max resid 0.0009212723 
## ... Similar to previous best
## Run 18 stress 0.1383294 
## ... Procrustes: rmse 0.001951796  max resid 0.003530348 
## ... Similar to previous best
## Run 19 stress 0.223134 
## Run 20 stress 0.2136644 
## *** Solution reached
## Run 0 stress 0.1396049 
## Run 1 stress 0.2004114 
## Run 2 stress 0.2004113 
## Run 3 stress 0.1554504 
## Run 4 stress 0.1396049 
## ... Procrustes: rmse 5.734123e-05  max resid 0.0001207403 
## ... Similar to previous best
## Run 5 stress 0.139605 
## ... Procrustes: rmse 0.0001539508  max resid 0.0003112719 
## ... Similar to previous best
## Run 6 stress 0.2847186 
## Run 7 stress 0.1396049 
## ... New best solution
## ... Procrustes: rmse 2.046337e-05  max resid 4.386856e-05 
## ... Similar to previous best
## Run 8 stress 0.1396049 
## ... Procrustes: rmse 9.114325e-06  max resid 1.837286e-05 
## ... Similar to previous best
## Run 9 stress 0.1396049 
## ... Procrustes: rmse 2.266558e-05  max resid 5.025931e-05 
## ... Similar to previous best
## Run 10 stress 0.1396049 
## ... Procrustes: rmse 4.119668e-06  max resid 8.658105e-06 
## ... Similar to previous best
## Run 11 stress 0.1396049 
## ... Procrustes: rmse 0.000104347  max resid 0.0002132518 
## ... Similar to previous best
## Run 12 stress 0.1554505 
## Run 13 stress 0.1554513 
## Run 14 stress 0.1941377 
## Run 15 stress 0.139605 
## ... Procrustes: rmse 0.0001882369  max resid 0.000378769 
## ... Similar to previous best
## Run 16 stress 0.1396049 
## ... Procrustes: rmse 0.0001488906  max resid 0.0003177032 
## ... Similar to previous best
## Run 17 stress 0.1396049 
## ... Procrustes: rmse 8.27646e-05  max resid 0.0001748531 
## ... Similar to previous best
## Run 18 stress 0.1396049 
## ... Procrustes: rmse 0.000143443  max resid 0.0003061739 
## ... Similar to previous best
## Run 19 stress 0.2340567 
## Run 20 stress 0.2603873 
## *** Solution reached
## Run 0 stress 0.04595665 
## Run 1 stress 0.08158088 
## Run 2 stress 0.08294231 
## Run 3 stress 0.3473089 
## Run 4 stress 0.04838974 
## Run 5 stress 0.05318677 
## Run 6 stress 0.04595612 
## ... New best solution
## ... Procrustes: rmse 0.001060725  max resid 0.002600651 
## ... Similar to previous best
## Run 7 stress 0.05318649 
## Run 8 stress 0.05106695 
## Run 9 stress 0.08373163 
## Run 10 stress 0.04595697 
## ... Procrustes: rmse 0.0002297223  max resid 0.0005445442 
## ... Similar to previous best
## Run 11 stress 0.08294096 
## Run 12 stress 0.08157914 
## Run 13 stress 0.04838885 
## Run 14 stress 0.08157822 
## Run 15 stress 0.08256976 
## Run 16 stress 0.05106339 
## Run 17 stress 0.05106757 
## Run 18 stress 0.04595626 
## ... Procrustes: rmse 4.395981e-05  max resid 0.0001025333 
## ... Similar to previous best
## Run 19 stress 0.04595633 
## ... Procrustes: rmse 6.689515e-05  max resid 0.0001611577 
## ... Similar to previous best
## Run 20 stress 0.05319174 
## *** Solution reached
## INFO [2020-08-20 16:22:28] Done.
## INFO [2020-08-20 16:22:28] Saving ...
## INFO [2020-08-20 16:22:30] Supplement Beta plots ...
## INFO [2020-08-20 16:22:30] Done.
## INFO [2020-08-20 16:22:30] Split table t50...
## INFO [2020-08-20 16:22:30] Done.
## [1] ""
## INFO [2020-08-20 16:22:30] No glom ...
## INFO [2020-08-20 16:22:30] Bray ...
## 
##  mutant sauvage 
##       6       6 
## INFO [2020-08-20 16:22:30] Done
## INFO [2020-08-20 16:22:30] Unifrac ...
## INFO [2020-08-20 16:22:30] Done
## INFO [2020-08-20 16:22:30] wunifrac ...
## INFO [2020-08-20 16:22:31] Done
## 
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1   0.06369 0.06369   3.118 0.03093 0.091908 .  
## souche     1   1.81185 1.81185  88.707 0.87981 0.000999 ***
## Residuals  9   0.18383 0.02043         0.08926             
## Total     11   2.05937                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
##               pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1  1.817719 75.21929 0.8826557   0.003      0.003   *
## 
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1   0.08686 0.08686   6.094 0.08776 0.010989 *  
## souche     1   0.77457 0.77457  54.339 0.78261 0.000999 ***
## Residuals  9   0.12829 0.01425         0.12962             
## Total     11   0.98972                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
##               pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1 0.7765885 36.43771 0.7846578   0.003      0.003   *
## 
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)   
## Depth      1   0.00813 0.00813   2.452 0.02254 0.141858   
## souche     1   0.32283 0.32283  97.317 0.89471 0.001998 **
## Residuals  9   0.02986 0.00332         0.08274            
## Total     11   0.36082                 1.00000            
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
##               pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1 0.3244241 89.13773 0.8991302   0.005      0.005   *
## INFO [2020-08-20 16:22:31] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 7.297422e-05 
## Run 1 stress 8.45789e-05 
## ... Procrustes: rmse 0.0001215438  max resid 0.0002227982 
## ... Similar to previous best
## Run 2 stress 9.207008e-05 
## ... Procrustes: rmse 6.328125e-05  max resid 0.0001237073 
## ... Similar to previous best
## Run 3 stress 9.829877e-05 
## ... Procrustes: rmse 0.0002645478  max resid 0.000608853 
## ... Similar to previous best
## Run 4 stress 9.716562e-05 
## ... Procrustes: rmse 0.0002436066  max resid 0.0006010068 
## ... Similar to previous best
## Run 5 stress 0.3058599 
## Run 6 stress 9.46279e-05 
## ... Procrustes: rmse 0.0002499175  max resid 0.0005774111 
## ... Similar to previous best
## Run 7 stress 8.757103e-05 
## ... Procrustes: rmse 6.716841e-05  max resid 0.0001251571 
## ... Similar to previous best
## Run 8 stress 9.195979e-05 
## ... Procrustes: rmse 8.073643e-05  max resid 0.0001739123 
## ... Similar to previous best
## Run 9 stress 0.3140419 
## Run 10 stress 9.586115e-05 
## ... Procrustes: rmse 0.0002378274  max resid 0.0005885461 
## ... Similar to previous best
## Run 11 stress 9.569161e-05 
## ... Procrustes: rmse 0.0002584231  max resid 0.0005962577 
## ... Similar to previous best
## Run 12 stress 9.926251e-05 
## ... Procrustes: rmse 0.0002480177  max resid 0.0006144776 
## ... Similar to previous best
## Run 13 stress 9.712931e-05 
## ... Procrustes: rmse 0.00022982  max resid 0.0005743353 
## ... Similar to previous best
## Run 14 stress 9.466725e-05 
## ... Procrustes: rmse 0.0001141341  max resid 0.0002334227 
## ... Similar to previous best
## Run 15 stress 9.52215e-05 
## ... Procrustes: rmse 0.0002329505  max resid 0.000578527 
## ... Similar to previous best
## Run 16 stress 9.940682e-05 
## ... Procrustes: rmse 7.611758e-05  max resid 0.000133508 
## ... Similar to previous best
## Run 17 stress 9.552615e-05 
## ... Procrustes: rmse 0.0002390993  max resid 0.0005949772 
## ... Similar to previous best
## Run 18 stress 8.147638e-05 
## ... Procrustes: rmse 6.517806e-05  max resid 0.0001300267 
## ... Similar to previous best
## Run 19 stress 9.97229e-05 
## ... Procrustes: rmse 0.0002669821  max resid 0.0006146439 
## ... Similar to previous best
## Run 20 stress 9.798424e-05 
## ... Procrustes: rmse 0.000263625  max resid 0.000607186 
## ... Similar to previous best
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 8.694326e-05 
## Run 1 stress 9.854982e-05 
## ... Procrustes: rmse 8.080336e-05  max resid 0.0001753304 
## ... Similar to previous best
## Run 2 stress 9.382942e-05 
## ... Procrustes: rmse 0.0001457875  max resid 0.0002709259 
## ... Similar to previous best
## Run 3 stress 8.898157e-05 
## ... Procrustes: rmse 0.000245596  max resid 0.0006366559 
## ... Similar to previous best
## Run 4 stress 9.411321e-05 
## ... Procrustes: rmse 0.00024977  max resid 0.0006441361 
## ... Similar to previous best
## Run 5 stress 9.942027e-05 
## ... Procrustes: rmse 0.0002530989  max resid 0.0006966849 
## ... Similar to previous best
## Run 6 stress 9.297371e-05 
## ... Procrustes: rmse 2.102032e-05  max resid 4.617638e-05 
## ... Similar to previous best
## Run 7 stress 9.894972e-05 
## ... Procrustes: rmse 0.000109759  max resid 0.0001972609 
## ... Similar to previous best
## Run 8 stress 9.007518e-05 
## ... Procrustes: rmse 6.146687e-05  max resid 0.0001615927 
## ... Similar to previous best
## Run 9 stress 9.900251e-05 
## ... Procrustes: rmse 0.0001511565  max resid 0.0003268108 
## ... Similar to previous best
## Run 10 stress 9.277227e-05 
## ... Procrustes: rmse 5.949235e-05  max resid 0.0001621835 
## ... Similar to previous best
## Run 11 stress 9.406521e-05 
## ... Procrustes: rmse 0.0002407634  max resid 0.0006704454 
## ... Similar to previous best
## Run 12 stress 9.894569e-05 
## ... Procrustes: rmse 8.11661e-05  max resid 0.000167026 
## ... Similar to previous best
## Run 13 stress 8.809663e-05 
## ... Procrustes: rmse 0.0002260571  max resid 0.0006346435 
## ... Similar to previous best
## Run 14 stress 9.781974e-05 
## ... Procrustes: rmse 0.0001281381  max resid 0.0002442458 
## ... Similar to previous best
## Run 15 stress 8.987769e-05 
## ... Procrustes: rmse 6.61466e-05  max resid 0.0001746144 
## ... Similar to previous best
## Run 16 stress 9.646128e-05 
## ... Procrustes: rmse 2.260404e-05  max resid 4.839915e-05 
## ... Similar to previous best
## Run 17 stress 9.249083e-05 
## ... Procrustes: rmse 0.0002362189  max resid 0.0006590408 
## ... Similar to previous best
## Run 18 stress 9.135082e-05 
## ... Procrustes: rmse 6.491662e-05  max resid 0.00018272 
## ... Similar to previous best
## Run 19 stress 9.665503e-05 
## ... Procrustes: rmse 0.0002372702  max resid 0.0006617 
## ... Similar to previous best
## Run 20 stress 9.649675e-05 
## ... Procrustes: rmse 0.0002438222  max resid 0.0006780024 
## ... Similar to previous best
## *** Solution reached
## Run 0 stress 9.276606e-05 
## Run 1 stress 9.963832e-05 
## ... Procrustes: rmse 6.473669e-05  max resid 0.0001693699 
## ... Similar to previous best
## Run 2 stress 9.903512e-05 
## ... Procrustes: rmse 0.0001688132  max resid 0.0003505997 
## ... Similar to previous best
## Run 3 stress 8.961392e-05 
## ... New best solution
## ... Procrustes: rmse 0.0001910005  max resid 0.0004478852 
## ... Similar to previous best
## Run 4 stress 9.865541e-05 
## ... Procrustes: rmse 0.0001994816  max resid 0.0004524879 
## ... Similar to previous best
## Run 5 stress 9.254986e-05 
## ... Procrustes: rmse 0.0001949915  max resid 0.0004590641 
## ... Similar to previous best
## Run 6 stress 9.060173e-05 
## ... Procrustes: rmse 0.0001908197  max resid 0.0004472175 
## ... Similar to previous best
## Run 7 stress 9.541876e-05 
## ... Procrustes: rmse 0.0002075558  max resid 0.0004750331 
## ... Similar to previous best
## Run 8 stress 9.966815e-05 
## ... Procrustes: rmse 0.0001633148  max resid 0.0004276814 
## ... Similar to previous best
## Run 9 stress 9.458347e-05 
## ... Procrustes: rmse 0.0001861985  max resid 0.0004133003 
## ... Similar to previous best
## Run 10 stress 9.947262e-05 
## ... Procrustes: rmse 0.0001894483  max resid 0.0004865836 
## ... Similar to previous best
## Run 11 stress 9.153207e-05 
## ... Procrustes: rmse 0.0001122695  max resid 0.0002880034 
## ... Similar to previous best
## Run 12 stress 9.714027e-05 
## ... Procrustes: rmse 0.0001138687  max resid 0.0002591533 
## ... Similar to previous best
## Run 13 stress 0.2395887 
## Run 14 stress 0.3358428 
## Run 15 stress 9.749958e-05 
## ... Procrustes: rmse 0.0001919343  max resid 0.0004278425 
## ... Similar to previous best
## Run 16 stress 9.837467e-05 
## ... Procrustes: rmse 0.0001113756  max resid 0.0002920375 
## ... Similar to previous best
## Run 17 stress 9.778157e-05 
## ... Procrustes: rmse 0.0001666583  max resid 0.0004155226 
## ... Similar to previous best
## Run 18 stress 8.942184e-05 
## ... New best solution
## ... Procrustes: rmse 0.0001457429  max resid 0.000271445 
## ... Similar to previous best
## Run 19 stress 9.537981e-05 
## ... Procrustes: rmse 0.000174662  max resid 0.0003249604 
## ... Similar to previous best
## Run 20 stress 9.798832e-05 
## ... Procrustes: rmse 0.000182358  max resid 0.000335553 
## ... Similar to previous best
## *** Solution reached
## Run 0 stress 0.001242547 
## Run 1 stress 9.71853e-05 
## ... New best solution
## ... Procrustes: rmse 0.007122439  max resid 0.01353808 
## Run 2 stress 0.002928554 
## Run 3 stress 0.001831015 
## Run 4 stress 0.00123311 
## Run 5 stress 0.001590412 
## Run 6 stress 9.871512e-05 
## ... Procrustes: rmse 0.0003809489  max resid 0.0007514137 
## ... Similar to previous best
## Run 7 stress 0.002946181 
## Run 8 stress 0.001691645 
## Run 9 stress 0.001060364 
## Run 10 stress 0.001268543 
## Run 11 stress 0.0006010893 
## Run 12 stress 9.232917e-05 
## ... New best solution
## ... Procrustes: rmse 0.0001333834  max resid 0.0002962656 
## ... Similar to previous best
## Run 13 stress 0.2625519 
## Run 14 stress 0.002211623 
## Run 15 stress 0.0009552186 
## Run 16 stress 9.388226e-05 
## ... Procrustes: rmse 0.0001730497  max resid 0.0004371434 
## ... Similar to previous best
## Run 17 stress 0.001895442 
## Run 18 stress 0.001596025 
## Run 19 stress 0.0007113803 
## Run 20 stress 0.0004222481 
## ... Procrustes: rmse 0.002380513  max resid 0.004630478 
## ... Similar to previous best
## *** Solution reached
## INFO [2020-08-20 16:22:31] Done.
## INFO [2020-08-20 16:22:31] Saving ...

## INFO [2020-08-20 16:22:33] Supplement Beta plots ...
## INFO [2020-08-20 16:22:33] Done.
## INFO [2020-08-20 16:22:33] Global1...
## [1] ""
## INFO [2020-08-20 16:22:33] No glom ...
## INFO [2020-08-20 16:22:33] Bray ...
##      souche
## temps mutant sauvage
##   t0       6       6
##   t50      6       6
## INFO [2020-08-20 16:22:34] Done
## INFO [2020-08-20 16:22:34] Unifrac ...
## INFO [2020-08-20 16:22:34] Done
## INFO [2020-08-20 16:22:34] wunifrac ...
## INFO [2020-08-20 16:22:34] Done
## 
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1    0.5075 0.50751  3.1218 0.06845 0.010989 *  
## temps      1    2.1846 2.18458 13.4380 0.29463 0.000999 ***
## souche     1    1.4711 1.47112  9.0493 0.19841 0.000999 ***
## Residuals 20    3.2514 0.16257         0.43851             
## Total     23    7.4146                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
##                       pairs Df SumsOfSqs   F.Model        R2 p.value p.adjusted
## 1   t0-sauvage vs t0-mutant  1  0.952842  4.640344 0.3169559   0.010     0.0100
## 2 t0-sauvage vs t50-sauvage  1  2.020676 28.967360 0.7433750   0.004     0.0048
## 3  t0-sauvage vs t50-mutant  1  2.197269 26.004113 0.7222540   0.003     0.0045
## 4  t0-mutant vs t50-sauvage  1  1.680832 11.591365 0.5368519   0.002     0.0045
## 5   t0-mutant vs t50-mutant  1  1.569713  9.826226 0.4956176   0.002     0.0045
## 6 t50-sauvage vs t50-mutant  1  1.817719 75.219295 0.8826557   0.003     0.0045
##   sig
## 1   *
## 2   *
## 3   *
## 4   *
## 5   *
## 6   *
## 
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1   0.14371 0.14371  2.4348 0.04803 0.049950 *  
## temps      1   1.03178 1.03178 17.4817 0.34487 0.000999 ***
## souche     1   0.63586 0.63586 10.7735 0.21254 0.000999 ***
## Residuals 20   1.18041 0.05902         0.39456             
## Total     23   2.99175                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
##                       pairs Df SumsOfSqs   F.Model        R2 p.value p.adjusted
## 1   t0-sauvage vs t0-mutant  1 0.2393236  3.315767 0.2490106   0.005      0.005
## 2 t0-sauvage vs t50-sauvage  1 0.5252573 11.751272 0.5402568   0.005      0.005
## 3  t0-sauvage vs t50-mutant  1 1.0634198 21.786254 0.6853986   0.004      0.005
## 4  t0-mutant vs t50-sauvage  1 0.6374245 14.266849 0.5879152   0.003      0.005
## 5   t0-mutant vs t50-mutant  1 0.8716882 17.865276 0.6411304   0.001      0.005
## 6 t50-sauvage vs t50-mutant  1 0.7765885 36.437712 0.7846578   0.002      0.005
##   sig
## 1   *
## 2   *
## 3   *
## 4   *
## 5   *
## 6   *
## 
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1   0.37184 0.37184  6.7259 0.15593 0.000999 ***
## temps      1   0.54053 0.54053  9.7773 0.22667 0.000999 ***
## souche     1   0.36665 0.36665  6.6321 0.15375 0.000999 ***
## Residuals 20   1.10568 0.05528         0.46366             
## Total     23   2.38470                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
##                       pairs Df SumsOfSqs   F.Model        R2 p.value p.adjusted
## 1   t0-sauvage vs t0-mutant  1 0.2990497  3.044468 0.2333915   0.045     0.0450
## 2 t0-sauvage vs t50-sauvage  1 0.5797822 10.671909 0.5162517   0.001     0.0030
## 3  t0-sauvage vs t50-mutant  1 0.7469215 12.936822 0.5640198   0.002     0.0030
## 4  t0-mutant vs t50-sauvage  1 0.3028129  6.616965 0.3982054   0.002     0.0030
## 5   t0-mutant vs t50-mutant  1 0.3699053  7.522786 0.4293145   0.001     0.0030
## 6 t50-sauvage vs t50-mutant  1 0.4009432 76.052249 0.8837915   0.004     0.0048
##   sig
## 1   .
## 2   *
## 3   *
## 4   *
## 5   *
## 6   *
## INFO [2020-08-20 16:22:34] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004764 
## Run 1 stress 0.1337253 
## Run 2 stress 0.1282052 
## Run 3 stress 0.1282057 
## Run 4 stress 0.1004882 
## ... Procrustes: rmse 0.00581795  max resid 0.02233252 
## Run 5 stress 0.1004882 
## ... Procrustes: rmse 0.005815793  max resid 0.02232645 
## Run 6 stress 0.1004764 
## ... New best solution
## ... Procrustes: rmse 9.5663e-06  max resid 3.522038e-05 
## ... Similar to previous best
## Run 7 stress 0.1278059 
## Run 8 stress 0.1004884 
## ... Procrustes: rmse 0.005827579  max resid 0.02235 
## Run 9 stress 0.1004882 
## ... Procrustes: rmse 0.005829094  max resid 0.02236715 
## Run 10 stress 0.1004882 
## ... Procrustes: rmse 0.005820554  max resid 0.02234192 
## Run 11 stress 0.1004764 
## ... Procrustes: rmse 4.844533e-05  max resid 0.000148011 
## ... Similar to previous best
## Run 12 stress 0.1004882 
## ... Procrustes: rmse 0.005806417  max resid 0.02231016 
## Run 13 stress 0.1004765 
## ... Procrustes: rmse 8.616688e-05  max resid 0.0002323718 
## ... Similar to previous best
## Run 14 stress 0.1004764 
## ... Procrustes: rmse 2.073449e-05  max resid 3.53979e-05 
## ... Similar to previous best
## Run 15 stress 0.1322893 
## Run 16 stress 0.1004882 
## ... Procrustes: rmse 0.005826637  max resid 0.02236303 
## Run 17 stress 0.1004764 
## ... Procrustes: rmse 2.217978e-05  max resid 3.975911e-05 
## ... Similar to previous best
## Run 18 stress 0.1004882 
## ... Procrustes: rmse 0.005826171  max resid 0.02235134 
## Run 19 stress 0.1004765 
## ... Procrustes: rmse 7.112848e-05  max resid 0.0002076316 
## ... Similar to previous best
## Run 20 stress 0.1004764 
## ... Procrustes: rmse 5.020109e-05  max resid 0.0001032207 
## ... Similar to previous best
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004882 
## Run 1 stress 0.1282054 
## Run 2 stress 0.1004764 
## ... New best solution
## ... Procrustes: rmse 0.005815739  max resid 0.02229399 
## Run 3 stress 0.1322874 
## Run 4 stress 0.1004883 
## ... Procrustes: rmse 0.005799368  max resid 0.02233062 
## Run 5 stress 0.1278333 
## Run 6 stress 0.1282048 
## Run 7 stress 0.1278136 
## Run 8 stress 0.1278166 
## Run 9 stress 0.1004764 
## ... Procrustes: rmse 1.375747e-05  max resid 2.676552e-05 
## ... Similar to previous best
## Run 10 stress 0.1004764 
## ... Procrustes: rmse 1.206258e-05  max resid 2.852253e-05 
## ... Similar to previous best
## Run 11 stress 0.1004764 
## ... Procrustes: rmse 1.697751e-05  max resid 5.177575e-05 
## ... Similar to previous best
## Run 12 stress 0.1004882 
## ... Procrustes: rmse 0.00581142  max resid 0.02230079 
## Run 13 stress 0.1282048 
## Run 14 stress 0.1004883 
## ... Procrustes: rmse 0.005833772  max resid 0.02232659 
## Run 15 stress 0.1004882 
## ... Procrustes: rmse 0.005824392  max resid 0.02233711 
## Run 16 stress 0.1004764 
## ... Procrustes: rmse 1.842712e-05  max resid 6.09228e-05 
## ... Similar to previous best
## Run 17 stress 0.1004882 
## ... Procrustes: rmse 0.005796345  max resid 0.02220223 
## Run 18 stress 0.1004765 
## ... Procrustes: rmse 6.12507e-05  max resid 0.0001541102 
## ... Similar to previous best
## Run 19 stress 0.1004882 
## ... Procrustes: rmse 0.005814297  max resid 0.02230969 
## Run 20 stress 0.1278114 
## *** Solution reached
## Run 0 stress 0.1230986 
## Run 1 stress 0.173498 
## Run 2 stress 0.1232368 
## ... Procrustes: rmse 0.008672617  max resid 0.03032075 
## Run 3 stress 0.1232368 
## ... Procrustes: rmse 0.008663607  max resid 0.0302901 
## Run 4 stress 0.1232368 
## ... Procrustes: rmse 0.008658079  max resid 0.03026862 
## Run 5 stress 0.1232368 
## ... Procrustes: rmse 0.008666702  max resid 0.03029997 
## Run 6 stress 0.1230986 
## ... Procrustes: rmse 7.354334e-05  max resid 0.0002143172 
## ... Similar to previous best
## Run 7 stress 0.123274 
## ... Procrustes: rmse 0.005402749  max resid 0.01785782 
## Run 8 stress 0.1232368 
## ... Procrustes: rmse 0.008658476  max resid 0.03027094 
## Run 9 stress 0.1230985 
## ... New best solution
## ... Procrustes: rmse 3.536316e-05  max resid 0.0001358091 
## ... Similar to previous best
## Run 10 stress 0.1232368 
## ... Procrustes: rmse 0.008666399  max resid 0.03030248 
## Run 11 stress 0.1232368 
## ... Procrustes: rmse 0.008661719  max resid 0.03028381 
## Run 12 stress 0.1671132 
## Run 13 stress 0.1671104 
## Run 14 stress 0.1232368 
## ... Procrustes: rmse 0.008673705  max resid 0.03032571 
## Run 15 stress 0.1232368 
## ... Procrustes: rmse 0.008676942  max resid 0.03033842 
## Run 16 stress 0.1232368 
## ... Procrustes: rmse 0.008670573  max resid 0.03031969 
## Run 17 stress 0.1232368 
## ... Procrustes: rmse 0.008667618  max resid 0.03030295 
## Run 18 stress 0.1669265 
## Run 19 stress 0.1734981 
## Run 20 stress 0.1232368 
## ... Procrustes: rmse 0.008673297  max resid 0.03032551 
## *** Solution reached
## Run 0 stress 0.08834803 
## Run 1 stress 0.08834753 
## ... New best solution
## ... Procrustes: rmse 0.0007319815  max resid 0.00298183 
## ... Similar to previous best
## Run 2 stress 0.09399784 
## Run 3 stress 0.0935419 
## Run 4 stress 0.08834743 
## ... New best solution
## ... Procrustes: rmse 0.0003040748  max resid 0.00120801 
## ... Similar to previous best
## Run 5 stress 0.08834736 
## ... New best solution
## ... Procrustes: rmse 0.0001113763  max resid 0.0004374654 
## ... Similar to previous best
## Run 6 stress 0.1353625 
## Run 7 stress 0.08834747 
## ... Procrustes: rmse 0.0001356072  max resid 0.0005634684 
## ... Similar to previous best
## Run 8 stress 0.08834776 
## ... Procrustes: rmse 0.0003060515  max resid 0.00100127 
## ... Similar to previous best
## Run 9 stress 0.08834815 
## ... Procrustes: rmse 0.0004330635  max resid 0.00184907 
## ... Similar to previous best
## Run 10 stress 0.0883478 
## ... Procrustes: rmse 0.0003098356  max resid 0.001320679 
## ... Similar to previous best
## Run 11 stress 0.08834728 
## ... New best solution
## ... Procrustes: rmse 8.683843e-05  max resid 0.0003166459 
## ... Similar to previous best
## Run 12 stress 0.08834728 
## ... New best solution
## ... Procrustes: rmse 8.439185e-05  max resid 0.0003479977 
## ... Similar to previous best
## Run 13 stress 0.1379554 
## Run 14 stress 0.1379528 
## Run 15 stress 0.08834785 
## ... Procrustes: rmse 0.0004566997  max resid 0.001869886 
## ... Similar to previous best
## Run 16 stress 0.09399776 
## Run 17 stress 0.1379538 
## Run 18 stress 0.08834927 
## ... Procrustes: rmse 0.0006699547  max resid 0.002737435 
## ... Similar to previous best
## Run 19 stress 0.09399662 
## Run 20 stress 0.08834845 
## ... Procrustes: rmse 0.0004899353  max resid 0.001999688 
## ... Similar to previous best
## *** Solution reached
## INFO [2020-08-20 16:22:35] Done.
## INFO [2020-08-20 16:22:35] Saving ...

## INFO [2020-08-20 16:22:38] Supplement Beta plots ...
## INFO [2020-08-20 16:22:38] Done.
## INFO [2020-08-20 16:22:38] Global2...
## [1] ""
## INFO [2020-08-20 16:22:38] No glom ...
## INFO [2020-08-20 16:22:38] Bray ...
## 
##  t0 t50 
##  12  12 
## INFO [2020-08-20 16:22:38] Done
## INFO [2020-08-20 16:22:38] Unifrac ...
## INFO [2020-08-20 16:22:38] Done
## INFO [2020-08-20 16:22:38] wunifrac ...
## INFO [2020-08-20 16:22:38] Done
## 
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1    0.5075 0.50751  2.2568 0.06845 0.060939 .  
## temps      1    2.1846 2.18458  9.7144 0.29463 0.000999 ***
## Residuals 21    4.7225 0.22488         0.63692             
## Total     23    7.4146                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
##       pairs Df SumsOfSqs  F.Model       R2 p.value p.adjusted sig
## 1 t0 vs t50  1  2.348965 10.20159 0.316804   0.001      0.001  **
## 
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1   0.13714 0.13714  1.5795 0.04693 0.155844    
## temps      1   0.96203 0.96203 11.0799 0.32918 0.000999 ***
## Residuals 21   1.82337 0.08683         0.62390             
## Total     23   2.92255                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
##       pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 t0 vs t50  1 0.9675817 10.88858 0.3310748   0.001      0.001  **
## 
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs  MeanSqs F.Model      R2   Pr(>F)    
## Depth      1   0.11324 0.113242  4.7188 0.14109 0.002997 ** 
## temps      1   0.18544 0.185436  7.7272 0.23103 0.000999 ***
## Residuals 21   0.50396 0.023998         0.62788             
## Total     23   0.80263                  1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
##       pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 t0 vs t50  1 0.2168864 8.146016 0.2702187   0.001      0.001  **
## INFO [2020-08-20 16:22:38] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004764 
## Run 1 stress 0.1004882 
## ... Procrustes: rmse 0.0058152  max resid 0.02231829 
## Run 2 stress 0.1282048 
## Run 3 stress 0.1004882 
## ... Procrustes: rmse 0.005806771  max resid 0.02229052 
## Run 4 stress 0.1004884 
## ... Procrustes: rmse 0.005777356  max resid 0.02224024 
## Run 5 stress 0.1278246 
## Run 6 stress 0.1004882 
## ... Procrustes: rmse 0.005818638  max resid 0.02234708 
## Run 7 stress 0.1332837 
## Run 8 stress 0.1004765 
## ... Procrustes: rmse 0.0001182885  max resid 0.0003080071 
## ... Similar to previous best
## Run 9 stress 0.1278094 
## Run 10 stress 0.1004765 
## ... Procrustes: rmse 0.0001079871  max resid 0.0002819903 
## ... Similar to previous best
## Run 11 stress 0.1332837 
## Run 12 stress 0.1316113 
## Run 13 stress 0.1337298 
## Run 14 stress 0.1004882 
## ... Procrustes: rmse 0.005798541  max resid 0.02228347 
## Run 15 stress 0.1004764 
## ... Procrustes: rmse 3.126013e-05  max resid 7.168403e-05 
## ... Similar to previous best
## Run 16 stress 0.1004766 
## ... Procrustes: rmse 5.155549e-05  max resid 0.0001446153 
## ... Similar to previous best
## Run 17 stress 0.1004882 
## ... Procrustes: rmse 0.005800221  max resid 0.02228243 
## Run 18 stress 0.1004764 
## ... Procrustes: rmse 8.623673e-06  max resid 1.750607e-05 
## ... Similar to previous best
## Run 19 stress 0.1004882 
## ... Procrustes: rmse 0.005793156  max resid 0.02226272 
## Run 20 stress 0.3840802 
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004882 
## Run 1 stress 0.1004764 
## ... New best solution
## ... Procrustes: rmse 0.005814918  max resid 0.02227738 
## Run 2 stress 0.1316123 
## Run 3 stress 0.1278166 
## Run 4 stress 0.1278225 
## Run 5 stress 0.1332155 
## Run 6 stress 0.1004766 
## ... Procrustes: rmse 9.205958e-05  max resid 0.0002457274 
## ... Similar to previous best
## Run 7 stress 0.1322907 
## Run 8 stress 0.1274643 
## Run 9 stress 0.1278115 
## Run 10 stress 0.1004882 
## ... Procrustes: rmse 0.005806634  max resid 0.02227474 
## Run 11 stress 0.1004882 
## ... Procrustes: rmse 0.005809967  max resid 0.02229281 
## Run 12 stress 0.1004764 
## ... New best solution
## ... Procrustes: rmse 8.370976e-05  max resid 0.0002461561 
## ... Similar to previous best
## Run 13 stress 0.1004882 
## ... Procrustes: rmse 0.005821153  max resid 0.0223582 
## Run 14 stress 0.1282054 
## Run 15 stress 0.1332827 
## Run 16 stress 0.1338151 
## Run 17 stress 0.1004882 
## ... Procrustes: rmse 0.005818853  max resid 0.0223352 
## Run 18 stress 0.1004766 
## ... Procrustes: rmse 0.0001268051  max resid 0.0003952689 
## ... Similar to previous best
## Run 19 stress 0.1275919 
## Run 20 stress 0.1004765 
## ... Procrustes: rmse 6.635743e-05  max resid 0.0001774361 
## ... Similar to previous best
## *** Solution reached
## Run 0 stress 0.1211578 
## Run 1 stress 0.1211578 
## ... Procrustes: rmse 2.714291e-06  max resid 5.639292e-06 
## ... Similar to previous best
## Run 2 stress 0.1660951 
## Run 3 stress 0.1220808 
## Run 4 stress 0.1211578 
## ... Procrustes: rmse 3.110957e-06  max resid 1.043545e-05 
## ... Similar to previous best
## Run 5 stress 0.1220804 
## Run 6 stress 0.1208049 
## ... New best solution
## ... Procrustes: rmse 0.01452926  max resid 0.05165115 
## Run 7 stress 0.1211578 
## ... Procrustes: rmse 0.01452871  max resid 0.05175567 
## Run 8 stress 0.1218747 
## Run 9 stress 0.1211578 
## ... Procrustes: rmse 0.01452911  max resid 0.05175412 
## Run 10 stress 0.120805 
## ... Procrustes: rmse 3.446316e-05  max resid 9.502088e-05 
## ... Similar to previous best
## Run 11 stress 0.166108 
## Run 12 stress 0.1220804 
## Run 13 stress 0.1220804 
## Run 14 stress 0.1211578 
## ... Procrustes: rmse 0.01453088  max resid 0.05175291 
## Run 15 stress 0.1224642 
## Run 16 stress 0.1227048 
## Run 17 stress 0.1223135 
## Run 18 stress 0.1218747 
## Run 19 stress 0.1211578 
## ... Procrustes: rmse 0.01452661  max resid 0.05175859 
## Run 20 stress 0.1729678 
## *** Solution reached
## Run 0 stress 0.07441356 
## Run 1 stress 0.08050991 
## Run 2 stress 0.07522281 
## Run 3 stress 0.07477326 
## ... Procrustes: rmse 0.00814833  max resid 0.0325014 
## Run 4 stress 0.09301933 
## Run 5 stress 0.07441314 
## ... New best solution
## ... Procrustes: rmse 0.0001825381  max resid 0.0004760459 
## ... Similar to previous best
## Run 6 stress 0.09333941 
## Run 7 stress 0.08075318 
## Run 8 stress 0.0747723 
## ... Procrustes: rmse 0.008351518  max resid 0.03396039 
## Run 9 stress 0.07522104 
## Run 10 stress 0.07441185 
## ... New best solution
## ... Procrustes: rmse 0.0003876495  max resid 0.001061552 
## ... Similar to previous best
## Run 11 stress 0.09302056 
## Run 12 stress 0.07441187 
## ... Procrustes: rmse 0.0002135307  max resid 0.0006217941 
## ... Similar to previous best
## Run 13 stress 0.07441387 
## ... Procrustes: rmse 0.001058677  max resid 0.002298426 
## ... Similar to previous best
## Run 14 stress 0.08075309 
## Run 15 stress 0.07997456 
## Run 16 stress 0.0744119 
## ... Procrustes: rmse 0.0005426132  max resid 0.00129187 
## ... Similar to previous best
## Run 17 stress 0.07477255 
## ... Procrustes: rmse 0.008507167  max resid 0.03478628 
## Run 18 stress 0.08046399 
## Run 19 stress 0.08050348 
## Run 20 stress 0.08043635 
## *** Solution reached
## INFO [2020-08-20 16:22:39] Done.
## INFO [2020-08-20 16:22:39] Saving ...

## INFO [2020-08-20 16:22:41] Supplement Beta plots ...
## INFO [2020-08-20 16:22:41] Done.
## INFO [2020-08-20 16:22:41] Global3...
## [1] ""
## INFO [2020-08-20 16:22:41] No glom ...
## INFO [2020-08-20 16:22:41] Bray ...
## 
##  mutant sauvage 
##      12      12 
## INFO [2020-08-20 16:22:41] Done
## INFO [2020-08-20 16:22:41] Unifrac ...
## INFO [2020-08-20 16:22:42] Done
## INFO [2020-08-20 16:22:42] wunifrac ...
## INFO [2020-08-20 16:22:42] Done
## 
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1    0.5075 0.50751  1.9574 0.06845 0.078921 .  
## souche     1    1.4622 1.46217  5.6393 0.19720 0.000999 ***
## Residuals 21    5.4449 0.25928         0.73435             
## Total     23    7.4146                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
##               pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1  1.529137 5.715979 0.2062341   0.001      0.001  **
## 
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)   
## Depth      1   0.14509 0.14509  1.3503 0.04777 0.250749   
## souche     1   0.63543 0.63543  5.9138 0.20923 0.003996 **
## Residuals 21   2.25640 0.10745         0.74299            
## Total     23   3.03692                 1.00000            
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
##               pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1 0.6717987 6.248969 0.2212105   0.001      0.001  **
## 
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1   0.19505 0.19505  2.7736 0.09195 0.024975 *  
## souche     1   0.44934 0.44934  6.3897 0.21184 0.000999 ***
## Residuals 21   1.47676 0.07032         0.69621             
## Total     23   2.12115                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
##               pairs Df SumsOfSqs  F.Model       R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1 0.4476574 5.884986 0.211045   0.001      0.001  **
## INFO [2020-08-20 16:22:42] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004764 
## Run 1 stress 0.1004764 
## ... Procrustes: rmse 2.847012e-05  max resid 7.721061e-05 
## ... Similar to previous best
## Run 2 stress 0.1332144 
## Run 3 stress 0.1004765 
## ... Procrustes: rmse 0.0001032707  max resid 0.000269979 
## ... Similar to previous best
## Run 4 stress 0.1278064 
## Run 5 stress 0.1004882 
## ... Procrustes: rmse 0.005821273  max resid 0.02233913 
## Run 6 stress 0.1004882 
## ... Procrustes: rmse 0.005816364  max resid 0.02232368 
## Run 7 stress 0.1004765 
## ... Procrustes: rmse 6.90138e-05  max resid 0.0001834775 
## ... Similar to previous best
## Run 8 stress 0.1004882 
## ... Procrustes: rmse 0.005814011  max resid 0.02231602 
## Run 9 stress 0.1322888 
## Run 10 stress 0.1282046 
## Run 11 stress 0.1004764 
## ... New best solution
## ... Procrustes: rmse 7.282923e-06  max resid 1.664746e-05 
## ... Similar to previous best
## Run 12 stress 0.1004883 
## ... Procrustes: rmse 0.005784473  max resid 0.02224248 
## Run 13 stress 0.1004764 
## ... Procrustes: rmse 1.456917e-05  max resid 3.850938e-05 
## ... Similar to previous best
## Run 14 stress 0.1004882 
## ... Procrustes: rmse 0.005846599  max resid 0.02244329 
## Run 15 stress 0.1372463 
## Run 16 stress 0.1278191 
## Run 17 stress 0.1004765 
## ... Procrustes: rmse 6.182907e-05  max resid 0.0001456336 
## ... Similar to previous best
## Run 18 stress 0.1004769 
## ... Procrustes: rmse 7.942311e-05  max resid 0.0002831982 
## ... Similar to previous best
## Run 19 stress 0.1275855 
## Run 20 stress 0.128205 
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004882 
## Run 1 stress 0.1004764 
## ... New best solution
## ... Procrustes: rmse 0.005815834  max resid 0.02229868 
## Run 2 stress 0.1004882 
## ... Procrustes: rmse 0.005807534  max resid 0.0222804 
## Run 3 stress 0.1278065 
## Run 4 stress 0.1004883 
## ... Procrustes: rmse 0.005823992  max resid 0.02238489 
## Run 5 stress 0.1004764 
## ... Procrustes: rmse 1.060961e-05  max resid 2.872919e-05 
## ... Similar to previous best
## Run 6 stress 0.1004765 
## ... Procrustes: rmse 7.285362e-05  max resid 0.0001739838 
## ... Similar to previous best
## Run 7 stress 0.1282046 
## Run 8 stress 0.1316111 
## Run 9 stress 0.1332147 
## Run 10 stress 0.1275819 
## Run 11 stress 0.1004764 
## ... Procrustes: rmse 6.523432e-06  max resid 2.205578e-05 
## ... Similar to previous best
## Run 12 stress 0.1004766 
## ... Procrustes: rmse 0.0001054406  max resid 0.0002899242 
## ... Similar to previous best
## Run 13 stress 0.1004764 
## ... Procrustes: rmse 4.547765e-06  max resid 8.111205e-06 
## ... Similar to previous best
## Run 14 stress 0.1004884 
## ... Procrustes: rmse 0.005826732  max resid 0.0223516 
## Run 15 stress 0.1004766 
## ... Procrustes: rmse 5.296991e-05  max resid 0.0001744325 
## ... Similar to previous best
## Run 16 stress 0.1004882 
## ... Procrustes: rmse 0.005813939  max resid 0.02231234 
## Run 17 stress 0.1004764 
## ... Procrustes: rmse 6.363861e-05  max resid 0.000133539 
## ... Similar to previous best
## Run 18 stress 0.1278136 
## Run 19 stress 0.1278183 
## Run 20 stress 0.1004882 
## ... Procrustes: rmse 0.005808462  max resid 0.02229637 
## *** Solution reached
## Run 0 stress 0.1228815 
## Run 1 stress 0.1225281 
## ... New best solution
## ... Procrustes: rmse 0.01736792  max resid 0.05438397 
## Run 2 stress 0.1228815 
## ... Procrustes: rmse 0.01736807  max resid 0.05454788 
## Run 3 stress 0.1228749 
## ... Procrustes: rmse 0.009103093  max resid 0.03405979 
## Run 4 stress 0.1237 
## Run 5 stress 0.1223269 
## ... New best solution
## ... Procrustes: rmse 0.008473442  max resid 0.02951088 
## Run 6 stress 0.1237001 
## Run 7 stress 0.1228816 
## Run 8 stress 0.1223269 
## ... Procrustes: rmse 1.202391e-05  max resid 3.908702e-05 
## ... Similar to previous best
## Run 9 stress 0.1237 
## Run 10 stress 0.1228749 
## Run 11 stress 0.1223269 
## ... Procrustes: rmse 1.380151e-05  max resid 4.682433e-05 
## ... Similar to previous best
## Run 12 stress 0.123224 
## Run 13 stress 0.3783049 
## Run 14 stress 0.1228749 
## Run 15 stress 0.1228815 
## Run 16 stress 0.1237 
## Run 17 stress 0.1228749 
## Run 18 stress 0.1225281 
## ... Procrustes: rmse 0.008478317  max resid 0.02949272 
## Run 19 stress 0.1225281 
## ... Procrustes: rmse 0.008474735  max resid 0.02947121 
## Run 20 stress 0.1228815 
## *** Solution reached
## Run 0 stress 0.09132816 
## Run 1 stress 0.1324532 
## Run 2 stress 0.2381793 
## Run 3 stress 0.09132823 
## ... Procrustes: rmse 9.584463e-05  max resid 0.0003505044 
## ... Similar to previous best
## Run 4 stress 0.0959093 
## Run 5 stress 0.1312552 
## Run 6 stress 0.0913284 
## ... Procrustes: rmse 4.735323e-05  max resid 0.0001520468 
## ... Similar to previous best
## Run 7 stress 0.09132813 
## ... New best solution
## ... Procrustes: rmse 0.0001364362  max resid 0.0004802557 
## ... Similar to previous best
## Run 8 stress 0.1603723 
## Run 9 stress 0.09132809 
## ... New best solution
## ... Procrustes: rmse 8.667887e-05  max resid 0.000337298 
## ... Similar to previous best
## Run 10 stress 0.1324533 
## Run 11 stress 0.131847 
## Run 12 stress 0.1678831 
## Run 13 stress 0.1579075 
## Run 14 stress 0.09132815 
## ... Procrustes: rmse 8.327058e-05  max resid 0.0003190981 
## ... Similar to previous best
## Run 15 stress 0.1578989 
## Run 16 stress 0.09590905 
## Run 17 stress 0.2320746 
## Run 18 stress 0.09633224 
## Run 19 stress 0.09633088 
## Run 20 stress 0.09132817 
## ... Procrustes: rmse 0.0001071494  max resid 0.0004339076 
## ... Similar to previous best
## *** Solution reached
## INFO [2020-08-20 16:22:43] Done.
## INFO [2020-08-20 16:22:43] Saving ...

## INFO [2020-08-20 16:22:45] Supplement Beta plots ...
## INFO [2020-08-20 16:22:45] Done.
## INFO [2020-08-20 16:22:45] Finish